Arrhythmia Detection
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Benchmarks
MIT-BIH AR
Most implemented
ECG Heartbeat Classification: A Deep Transferable Representation
ECG arrhythmia classification using a 2-D convolutional neural network
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
Papers
Toward Energy-Efficient and Low-Power Arrhythmia Detection for Wearable Devices
Cardiovascular diseases are the leading cause of death worldwide, and conditions such as arrhythmia often require long-term monitoring for effective detection and diagnosis. However, current wearable monitoring devices a…
Arrhythmia DetectionSL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Modeling long-sequence medical time series data, such as electrocardiograms (ECG), poses significant challenges due to high sampling rates, multichannel signal complexity, inherent noise, and limited labeled data. While …
Self-Supervised LearningDomain GeneralizationArrhythmia DetectionContrastive LearningArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems
Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems. We develop INT8 quantized …
Arrhythmia DetectionHeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection
While Deep Learning (DL) enhances automated electrocardiogram (ECG) analysis, clinical deployment is hindered by class imbalance and the generalization gap. This paper presents HeartBeatAI, a deep learning framework comb…
Domain GeneralizationArrhythmia DetectionECG ClassificationDeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition
Beat-level Electrocardiography (ECG) arrhythmia detection aims to assign an arrhythmia class to each beat in a recording, yet many existing systems treat beats as isolated local instances. This is limiting because beat l…
Arrhythmia DetectionDecision MakingTowards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables
Cardiovascular disease remains the leading cause of death worldwide, and early detection of arrhythmias through continuous ECG monitoring on wearable devices can prevent life-threatening events. Federated Learning (FL) e…
Arrhythmia DetectionFederated Learning